Fintech Pitch

● About me: Zulfikar Moinuddin Ahmed. Princeton math, worked at Lehman, a commodity fund Gresham Investment Mgmt, a pharma biotech (Predicant), a tech startup Babel Research

● I have Medium frequency price prediction for currencies and commodities (1m-15m) using AI techniques with spectacular results with retail transaction costs

● This is a plan to capture the medium frequency sector for alpha strategies via a technology company rather than traditional hedge fund

Medium Frequency High Capacity Algorithmic Strategies

● High Frequency Trading (HFT) in equities and forex depend on hardware colocation for sub-15 microsecond execution

● Total HFT profits peaked at $7.2 billion in 2006 and dropped to $1 billion in 2012

● Medium Frequency may be defined in the 1 min – 1h range

● Machine Learning is effective in HFT range and extends to Medium Frequency

Executive Summary

Trends in Multiple Scales

● Technical Analysis Traders/Chartists are intuitively aware of price behaviour at multiple time scales, 1 second, 1 minute, 5 minutes etc.

● Technical trading rules therefore often involve multiple indicators such as MACD, RSI etc. where buy/sell signals are generated by combination of overbought and momentum

● Technical indicators for a single scale do not automatically produce measures at multiple scales

● Signal decomposition to multiple time scales is naturally handled by Discrete Wavelet Transforms

● Trading Strategies that do not address multiple scales lead to increased probability of inaccurate price change direction prediction

Wavelet Decomposition Basics

● Discrete Wavelet Transforms have been used successfully in signal and image processing for feature extraction at multiple size scales. The basic structure is repeated high/low pass filters

Technical Indicators and Machine Learning Simultaneously at Multiple

Scales● Machine Learning algorithm are naturally applied to learn future

price changes based on a series of past data and indicators● Data: (x,y) where x is an qxp matrix of past prices and other

variables (e.g. technical indicators) and y is the future price change

● Supervised learning algorithms train an algorithm on {(x_1,y_1),…,(x_N,y_N)} and is used to predict (x’,y’) on new data x’ to predict y’

● These algorithms applied directly to prices at a given frequency, say 1 day, lead to very noisy predictions. They could be useful in higher frequencies but are generally not extremely satisfactory

Our Solution: Combine predictions at multiple time scales

● We employ the standard machine learning prediction strategy with technical indicators as additional variables at multiple time scales simultaneously and combine the results

● Wavelet Decomposition ‘details’ at multiple scales is natural data for predictions at multiple scales

● This strategy produces sufficiently accurate price change predictions for viable trading strategies for actual data for currencies and commodity futures in the 1-minute and 5-minute time scales

Results: Equity curves of backtests for July 2017 last 2000 minutes

Energy Results

Agriculturals Results

Machine Learning Algorithm

● Glmnet (but ther regression machines produce comparable results)

● Use machine learning algorithm to predict changes in wavelet details at multiple scales

● R package ‘mlr’ for tuning hyperparameters● Plan to study possible improvements by other

algorithms

Results with realistic transaction costs: $0.85 crude oil 0.1 bp

USDJPY

Proposed Trade Execution Stages

● Initially automate trade execution with retail broker Interactive Brokers

● Automatic execution code is done with R package ‘Ibrokers’

● After we have pilot portfolio completed and we can scale up we can outsource order management and trade execution to a high-end platform (1.5-2.5 microsecond execution) such as Finetix iX-eCute or another off-the-shelf system

Sample Startup Plan

● Manage a portfolio of currencies and commodity futures strategy on a small scale using Interactive Brokers

● Assemble team and scale up after requisite period to show results on live trading sufficient to elicit interest of hedge fund investors (institutions/private equity)

● All focus on medium frequency AI and statistical analysis strategies

● The goal is to SATURATE the medium frequency space eventually.